{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/gwf25/Desktop/Table 3.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 7 Dec 2015, 10:07:12

{com}. do "/Users/gwf25/Dropbox/research/religion/final code/Table 3.do"
{txt}
{com}. clear all
{txt}
{com}. set mem 50m
{txt}(51200k)

{com}. set more off
{txt}
{com}. 
. use "/Users/gwf25/Dropbox/research/religion/final code/religion_all.dta"
{txt}
{com}. 
. //  Begin definition of variables
. 
. ***Drop subjects who thought the experiment was about religion
. gen id = _n
{txt}
{com}. drop if id == 98 | id == 176 | id == 194 | id == 383
{txt}(4 observations deleted)

{com}. 
. ***Drop subjects who incorrectly completed the priming task. This includes subjects who leave more than half the responses blank. The following subjects
. ***all left at least questions #2-#7 blank in the sentence unscrambling task. 
. drop if id==7 | id==719 | id==740 | id==762 | id==940
{txt}(5 observations deleted)

{com}. 
. ***An error led to some subjects seeing both the control and religion salient sentence unscrambling tasks. Here, we drop those subjects.
. drop if prime_diff == 1
{txt}(2 observations deleted)

{com}. 
. ***"skipped" is a dummy variable for whether subjects skip the question that asks their religion. If they skip this question, we drop them from the sample 
. ***and if not, we assign a dummy variable to indicate the treatment group (religion salient or control) that subject belongs to.
. gen skipped=0
{txt}
{com}. replace skipped=1 if  s10q15==""
{txt}(21 real changes made)

{com}. gen treatR=.
{txt}(1033 missing values generated)

{com}. replace treatR=religion if skipped==0
{txt}(1012 real changes made)

{com}. drop if skipped==1
{txt}(21 observations deleted)

{com}. 
. ***Define religion
. gen relig=.
{txt}(1012 missing values generated)

{com}. 
. ***Note: 1 = protestant or other christian, 2 = catholic, 3 = jewish, 4 = agnostic/atheist
. replace relig=1 if (s10q15=="Christian - Other (please specify below)" | s10q15=="Christian - Protestant (please specify denomination below)")
{txt}(264 real changes made)

{com}. replace relig=2 if s10q15=="Christian - Catholic"
{txt}(199 real changes made)

{com}. replace relig=3 if s10q15=="Jewish (Orthodox/Reformed/etc.)" | s10q15=="Jewish (Orthodox/Reform/etc.)"
{txt}(95 real changes made)

{com}. replace relig=4 if (s10q15=="Agnostic" | s10q15=="Atheist")
{txt}(269 real changes made)

{com}. 
. ***Drop Mormon/Othodox Christians from the sample
. drop if s10q15sp == "Greek Orthodox"
{txt}(2 observations deleted)

{com}. drop if s10q15sp == "Russian Othrodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "greek orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Orthodox Christian"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthdox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "christian orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthodox Christian"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Russian orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Church of Jesus Christ of Latter Day Saints"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthodox"
{txt}(0 observations deleted)

{com}. 
. drop id
{txt}
{com}. // End of variables
. 
. gen id = _n
{txt}
{com}. 
. ***Create the divine punishment variable
. gen divinepunish = s10q18sp4
{txt}(179 missing values generated)

{com}. egen divinepunish_std1 = std(divinepunish) if relig == 1
{txt}(796 missing values generated)

{com}. egen divinepunish_std2 = std(divinepunish) if relig == 2
{txt}(838 missing values generated)

{com}. egen divinepunish_std3 = std(divinepunish) if relig == 3
{txt}(923 missing values generated)

{com}. egen divinepunish_std4 = std(divinepunish) if relig == 4
{txt}(775 missing values generated)

{com}. gen divinepunish_std1_R = divinepunish_std1 * treatR if relig == 1
{txt}(796 missing values generated)

{com}. gen divinepunish_std2_R = divinepunish_std2 * treatR if relig == 2
{txt}(838 missing values generated)

{com}. gen divinepunish_std3_R = divinepunish_std3 * treatR if relig == 3
{txt}(923 missing values generated)

{com}. gen divinepunish_std4_R = divinepunish_std4 * treatR if relig == 4
{txt}(775 missing values generated)

{com}. 
. ***Create the median religious service attendance variable
. gen religserv_freq = .
{txt}(1002 missing values generated)

{com}. replace religserv_freq = 1 if s10q16 == "Never"
{txt}(315 real changes made)

{com}. replace religserv_freq = 2 if s10q16 == "Less than once a month"
{txt}(388 real changes made)

{com}. replace religserv_freq = 3 if s10q16 == "Once a month"
{txt}(62 real changes made)

{com}. replace religserv_freq = 4 if s10q16 == "A few times a month"
{txt}(92 real changes made)

{com}. replace religserv_freq = 5 if s10q16 == "Once a week"
{txt}(102 real changes made)

{com}. replace religserv_freq = 6 if s10q16 == "A few times a week"
{txt}(32 real changes made)

{com}. replace religserv_freq = 7 if s10q16 == "Once a day"
{txt}(5 real changes made)

{com}. replace religserv_freq = 8 if s10q16 == "More than once a day"
{txt}(4 real changes made)

{com}. 
. egen median1 = median(religserv_freq) if relig == 1
{txt}(748 missing values generated)

{com}. gen religserv_freq1_median = .
{txt}(1002 missing values generated)

{com}. replace religserv_freq1_median = 1 if religserv_freq > median1
{txt}(116 real changes made)

{com}. replace religserv_freq1_median = 0 if religserv_freq <= median1
{txt}(886 real changes made)

{com}. 
. egen median2 = median(religserv_freq) if relig == 2
{txt}(803 missing values generated)

{com}. gen religserv_freq2_median = .
{txt}(1002 missing values generated)

{com}. replace religserv_freq2_median = 1 if religserv_freq > median2
{txt}(93 real changes made)

{com}. replace religserv_freq2_median = 0 if religserv_freq <= median2
{txt}(909 real changes made)

{com}. 
. egen median3 = median(religserv_freq) if relig == 3
{txt}(907 missing values generated)

{com}. gen religserv_freq3_median = .
{txt}(1002 missing values generated)

{com}. replace religserv_freq3_median = 1 if religserv_freq > median3
{txt}(16 real changes made)

{com}. replace religserv_freq3_median = 0 if religserv_freq <= median3
{txt}(986 real changes made)

{com}. 
. egen median4 = median(religserv_freq) if relig == 4
{txt}(733 missing values generated)

{com}. gen religserv_freq4_median = .
{txt}(1002 missing values generated)

{com}. replace religserv_freq4_median = 1 if religserv_freq > median4
{txt}(77 real changes made)

{com}. replace religserv_freq4_median = 0 if religserv_freq <= median4
{txt}(925 real changes made)

{com}. 
. gen religserv_freq1_median_R = treatR * religserv_freq1_median if relig == 1
{txt}(748 missing values generated)

{com}. gen religserv_freq2_median_R = treatR * religserv_freq2_median if relig == 2
{txt}(803 missing values generated)

{com}. gen religserv_freq3_median_R = treatR * religserv_freq3_median if relig == 3
{txt}(907 missing values generated)

{com}. gen religserv_freq4_median_R = treatR * religserv_freq4_median if relig == 4
{txt}(733 missing values generated)

{com}. 
. ***Public good contribution
. rename s6 contribute
{txt}
{com}. 
. reg contribute treatR divinepunish_std1_R divinepunish_std1 if relig == 1, r

{txt}Linear regression                                      Number of obs ={res}     124
                                                       {txt}F(  3,   120) ={res}    2.55
                                                       {txt}Prob > F      = {res} 0.0586
                                                       {txt}R-squared     = {res} 0.0605
                                                       {txt}Root MSE      = {res} .40125

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  contribute{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} .1411741{col 26}{space 2}  .071887{col 37}{space 1}    1.96{col 46}{space 3}0.052{col 54}{space 4}-.0011571{col 67}{space 3} .2835054
{txt}divinepu~1_R {c |}{col 14}{res}{space 2}  .114234{col 26}{space 2} .0729201{col 37}{space 1}    1.57{col 46}{space 3}0.120{col 54}{space 4}-.0301427{col 67}{space 3} .2586108
{txt}divinepuni~1 {c |}{col 14}{res}{space 2}-.0936506{col 26}{space 2} .0493464{col 37}{space 1}   -1.90{col 46}{space 3}0.060{col 54}{space 4} -.191353{col 67}{space 3} .0040517
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5173825{col 26}{space 2} .0528463{col 37}{space 1}    9.79{col 46}{space 3}0.000{col 54}{space 4} .4127505{col 67}{space 3} .6220144
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg contribute treatR religserv_freq1_median_R religserv_freq1_median if relig == 1, r

{txt}Linear regression                                      Number of obs ={res}     171
                                                       {txt}F(  3,   167) ={res}    3.42
                                                       {txt}Prob > F      = {res} 0.0187
                                                       {txt}R-squared     = {res} 0.0578
                                                       {txt}Root MSE      = {res} .39978

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  contribute{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} .1372293{col 26}{space 2} .0855269{col 37}{space 1}    1.60{col 46}{space 3}0.110{col 54}{space 4}-.0316241{col 67}{space 3} .3060826
{txt}r~1_median_R {c |}{col 14}{res}{space 2} .0201623{col 26}{space 2} .1231842{col 37}{space 1}    0.16{col 46}{space 3}0.870{col 54}{space 4}-.2230367{col 67}{space 3} .2633613
{txt}rel~1_median {c |}{col 14}{res}{space 2}-.1275893{col 26}{space 2}  .093483{col 37}{space 1}   -1.36{col 46}{space 3}0.174{col 54}{space 4}  -.31215{col 67}{space 3} .0569714
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5931707{col 26}{space 2} .0674575{col 37}{space 1}    8.79{col 46}{space 3}0.000{col 54}{space 4} .4599913{col 67}{space 3} .7263502
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg contribute treatR divinepunish_std2_R divinepunish_std2 if relig == 2, r

{txt}Linear regression                                      Number of obs ={res}     103
                                                       {txt}F(  3,    99) ={res}    2.01
                                                       {txt}Prob > F      = {res} 0.1168
                                                       {txt}R-squared     = {res} 0.0542
                                                       {txt}Root MSE      = {res}  .4068

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  contribute{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2}-.1764917{col 26}{space 2} .0798444{col 37}{space 1}   -2.21{col 46}{space 3}0.029{col 54}{space 4}-.3349203{col 67}{space 3}-.0180631
{txt}divinepu~2_R {c |}{col 14}{res}{space 2}-.0640262{col 26}{space 2} .0770613{col 37}{space 1}   -0.83{col 46}{space 3}0.408{col 54}{space 4}-.2169326{col 67}{space 3} .0888802
{txt}divinepuni~2 {c |}{col 14}{res}{space 2} .0360711{col 26}{space 2} .0531881{col 37}{space 1}    0.68{col 46}{space 3}0.499{col 54}{space 4}-.0694657{col 67}{space 3} .1416079
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7060127{col 26}{space 2}  .057726{col 37}{space 1}   12.23{col 46}{space 3}0.000{col 54}{space 4} .5914718{col 67}{space 3} .8205536
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg contribute treatR religserv_freq2_median_R religserv_freq2_median if relig == 2, r

{txt}Linear regression                                      Number of obs ={res}     138
                                                       {txt}F(  3,   134) ={res}    2.39
                                                       {txt}Prob > F      = {res} 0.0711
                                                       {txt}R-squared     = {res} 0.0505
                                                       {txt}Root MSE      = {res} .40517

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  contribute{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} -.197386{col 26}{space 2} .1030986{col 37}{space 1}   -1.91{col 46}{space 3}0.058{col 54}{space 4}-.4012971{col 67}{space 3} .0065252
{txt}r~2_median_R {c |}{col 14}{res}{space 2} .0284097{col 26}{space 2} .1378168{col 37}{space 1}    0.21{col 46}{space 3}0.837{col 54}{space 4} -.244168{col 67}{space 3} .3009874
{txt}rel~2_median {c |}{col 14}{res}{space 2}  .001697{col 26}{space 2} .0979289{col 37}{space 1}    0.02{col 46}{space 3}0.986{col 54}{space 4}-.1919893{col 67}{space 3} .1953832
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7013333{col 26}{space 2} .0743548{col 37}{space 1}    9.43{col 46}{space 3}0.000{col 54}{space 4} .5542725{col 67}{space 3} .8483942
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. 
. ***Risk aversion
. 
. ***Drop those subjects who don't take part in the risk preference section
. drop if s3q1 == .
{txt}(596 observations deleted)

{com}. 
. ***Generate upper limit for risk with small amounts
. gen risk1=.
{txt}(406 missing values generated)

{com}. replace risk1=(1.6*0.5-1) if s3q1==1
{txt}(37 real changes made)

{com}. gen risk2=.
{txt}(406 missing values generated)

{com}. replace risk2=(2*0.5-1) if s3q2==1
{txt}(111 real changes made)

{com}. gen risk3=.
{txt}(406 missing values generated)

{com}. replace risk3=(2.4*0.5-1) if s3q3==1
{txt}(244 real changes made)

{com}. gen risk4=.
{txt}(406 missing values generated)

{com}. replace risk4=(2.8*0.5-1) if s3q4==1
{txt}(342 real changes made)

{com}. gen risk5=.
{txt}(406 missing values generated)

{com}. replace risk5=(3.2*0.5-1) if s3q5==1
{txt}(377 real changes made)

{com}. gen risk6=.
{txt}(406 missing values generated)

{com}. replace risk6=(3.6*0.5-1) if s3q6==1
{txt}(390 real changes made)

{com}. 
. ***Choose the upper limit for the first time a subject chooses the risky asset with small amounts.
. gen reservationrisk1= min(risk1,risk2,risk3,risk4,risk5,risk6)
{txt}(11 missing values generated)

{com}. 
. ***Generate upper limit for risk with large amounts
. gen risk7=.
{txt}(406 missing values generated)

{com}. replace risk7=(160*0.5-100)/100 if s4q1==1
{txt}(19 real changes made)

{com}. gen risk8=.
{txt}(406 missing values generated)

{com}. replace risk8=(200*0.5-100)/100 if s4q2==1
{txt}(51 real changes made)

{com}. gen risk9=.
{txt}(406 missing values generated)

{com}. replace risk9=(240*0.5-100)/100 if s4q3==1
{txt}(137 real changes made)

{com}. gen risk10=.
{txt}(406 missing values generated)

{com}. replace risk10=(280*0.5-100)/100 if s4q4==1
{txt}(224 real changes made)

{com}. gen risk11=.
{txt}(406 missing values generated)

{com}. replace risk11=(320*0.5-100)/100 if s4q5==1
{txt}(283 real changes made)

{com}. gen risk12=.
{txt}(406 missing values generated)

{com}. replace risk12=(360*0.5-100)/100 if s4q6==1
{txt}(298 real changes made)

{com}. 
. ***Choose the upper limit for the first time a subject chooses the risky asset with large amounts.
. gen reservationrisk2= min(risk7,risk8,risk9,risk10,risk11,risk12)
{txt}(102 missing values generated)

{com}. 
. ***Create two entries per subject. One is for small amounts and the other is for large amounts. riskchoice indicates whether it is a small or large stake gamble. 
. reshape long reservationrisk, i(id) j(riskchoice)
{txt}(note: j = 1 2)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}     406   {txt}->{res}     812
{txt}Number of variables            {res}     240   {txt}->{res}     240
{txt}j variable (2 values)                     ->   {res}riskchoice
{txt}xij variables:
      {res}reservationrisk1 reservationrisk2   {txt}->   {res}reservationrisk
{txt}{hline 77}

{com}. 
. ***largestake is a dummy where a 1 indicates risk choices with large amounts and a 0 indicates risk choices with small amounts.
. gen largestake=0
{txt}
{com}. replace largestake= 1 if riskchoice==2 
{txt}(406 real changes made)

{com}. 
. ***Recall reservationrisk indicates the upper limit. Rename this variable risku and create another variable, riskl, which will indicate the lower limit.
. rename reservationrisk risk
{txt}
{com}. gen riskl=.
{txt}(812 missing values generated)

{com}. gen risku=risk
{txt}(113 missing values generated)

{com}. 
. ***Fill in values for the lower limit.
. ***Note that even if someone always chose the safe option, they are assigned a missing value for the upper limit and the highest possible value we ask about for the lower limit. So they are properly taken care of.
. replace riskl=. if risk < -.2
{txt}(0 real changes made)

{com}. replace riskl=(1.6*0.5-1) if risk == 0
{txt}(108 real changes made)

{com}. replace riskl=(2*0.5-1) if risk > .1 & risk < .3
{txt}(225 real changes made)

{com}. replace riskl=(2.4*0.5-1) if risk > .3 & risk < .5
{txt}(187 real changes made)

{com}. replace riskl=(2.8*0.5-1) if risk > .5 & risk < .7
{txt}(97 real changes made)

{com}. replace riskl=(3.2*0.5-1) if risk > .7 & risk < .9
{txt}(26 real changes made)

{com}. replace riskl=(3.6*0.5-1) if risk == . & s4q1 ~= .
{txt}(113 real changes made)

{com}. 
. intreg riskl risku treatR divinepunish_std2_R divinepunish_std2 largestake if relig==2, cluster(id)

{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-163.27297}  
Iteration 1:{space 3}log pseudolikelihood = {res:-159.91562}  
Iteration 2:{space 3}log pseudolikelihood = {res:-159.86565}  
Iteration 3:{space 3}log pseudolikelihood = {res:-159.86563}  
Iteration 4:{space 3}log pseudolikelihood = {res:-159.86563}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-151.00793}  
Iteration 1:{space 3}log pseudolikelihood = {res:-147.89419}  
Iteration 2:{space 3}log pseudolikelihood = {res:-147.87172}  
Iteration 3:{space 3}log pseudolikelihood = {res:-147.87171}  
{res}
{txt}Interval regression{col 51}Number of obs{col 67}= {res}        84
{txt}{col 51}Wald chi2({res}4{txt}){col 67}= {res}     26.55
{txt}Log pseudolikelihood = {res}-147.87171{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 78:(Std. Err. adjusted for {res:42} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2}-.1258891{col 26}{space 2} .0882892{col 37}{space 1}   -1.43{col 46}{space 3}0.154{col 54}{space 4}-.2989328{col 67}{space 3} .0471546
{txt}divinepu~2_R {c |}{col 14}{res}{space 2}-.0768672{col 26}{space 2} .0921037{col 37}{space 1}   -0.83{col 46}{space 3}0.404{col 54}{space 4}-.2573872{col 67}{space 3} .1036528
{txt}divinepuni~2 {c |}{col 14}{res}{space 2} .0611659{col 26}{space 2} .0561184{col 37}{space 1}    1.09{col 46}{space 3}0.276{col 54}{space 4}-.0488241{col 67}{space 3} .1711558
{txt}{space 2}largestake {c |}{col 14}{res}{space 2} .3762328{col 26}{space 2} .0763492{col 37}{space 1}    4.93{col 46}{space 3}0.000{col 54}{space 4} .2265911{col 67}{space 3} .5258744
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2005807{col 26}{space 2} .0567796{col 37}{space 1}    3.53{col 46}{space 3}0.000{col 54}{space 4} .0892947{col 67}{space 3} .3118667
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    /lnsigma{col 14}{c |}{res}{space 2}-1.055284{col 26}{space 2} .0824915{col 37}{space 1}  -12.79{col 46}{space 3}0.000{col 54}{space 4}-1.216964{col 67}{space 3}-.8936035
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       sigma{col 14}{c |}{res}{space 2} .3480936{col 26}{space 2} .0287148{col 54}{space 4} .2961278{col 67}{space 3} .4091786
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

  Observation summary:{col 24}{res}        3{col 34}{txt} left-censored observations
{col 24}{res}        0{col 34}{txt}    uncensored observations
{col 24}{res}       16{col 34}{txt}right-censored observations
{col 24}{res}       65{col 34}{txt}      interval observations

{com}. intreg riskl risku treatR religserv_freq2_median_R religserv_freq2_median largestake if relig==2, cluster(id)

{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-297.30458}  
Iteration 1:{space 3}log pseudolikelihood = {res:-291.10031}  
Iteration 2:{space 3}log pseudolikelihood = {res:-290.99546}  
Iteration 3:{space 3}log pseudolikelihood = {res:-290.99542}  
Iteration 4:{space 3}log pseudolikelihood = {res:-290.99542}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-281.78773}  
Iteration 1:{space 3}log pseudolikelihood = {res:-275.89248}  
Iteration 2:{space 3}log pseudolikelihood = {res:-275.82011}  
Iteration 3:{space 3}log pseudolikelihood = {res:-275.82009}  
{res}
{txt}Interval regression{col 51}Number of obs{col 67}= {res}       154
{txt}{col 51}Wald chi2({res}4{txt}){col 67}= {res}     41.07
{txt}Log pseudolikelihood = {res}-275.82009{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 78:(Std. Err. adjusted for {res:77} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2}-.1467418{col 26}{space 2} .0926916{col 37}{space 1}   -1.58{col 46}{space 3}0.113{col 54}{space 4} -.328414{col 67}{space 3} .0349305
{txt}r~2_median_R {c |}{col 14}{res}{space 2} .0823073{col 26}{space 2} .1324706{col 37}{space 1}    0.62{col 46}{space 3}0.534{col 54}{space 4}-.1773303{col 67}{space 3}  .341945
{txt}rel~2_median {c |}{col 14}{res}{space 2} .0522964{col 26}{space 2} .1006109{col 37}{space 1}    0.52{col 46}{space 3}0.603{col 54}{space 4}-.1448974{col 67}{space 3} .2494901
{txt}{space 2}largestake {c |}{col 14}{res}{space 2} .3048275{col 26}{space 2} .0516721{col 37}{space 1}    5.90{col 46}{space 3}0.000{col 54}{space 4} .2035521{col 67}{space 3} .4061028
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1852777{col 26}{space 2}  .064792{col 37}{space 1}    2.86{col 46}{space 3}0.004{col 54}{space 4} .0582876{col 67}{space 3} .3122677
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    /lnsigma{col 14}{c |}{res}{space 2} -1.03784{col 26}{space 2} .0801851{col 37}{space 1}  -12.94{col 46}{space 3}0.000{col 54}{space 4}   -1.195{col 67}{space 3}-.8806797
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       sigma{col 14}{c |}{res}{space 2} .3542191{col 26}{space 2} .0284031{col 54}{space 4} .3027041{col 67}{space 3} .4145011
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

  Observation summary:{col 24}{res}       10{col 34}{txt} left-censored observations
{col 24}{res}        0{col 34}{txt}    uncensored observations
{col 24}{res}       25{col 34}{txt}right-censored observations
{col 24}{res}      119{col 34}{txt}      interval observations

{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/gwf25/Desktop/Table 3.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 7 Dec 2015, 10:07:19
{txt}{.-}
{smcl}
{txt}{sf}{ul off}